<![CDATA[Data monetization has emerged as a key revenue strategy for startups in a competitive digital economy, though challenges like data fragmentation, quality issues, and privacy laws often arise. This literature-based study proposes an adaptive data governance conceptual framework for startups, informed by DMBOK v2. Utilizing a Systematic Literature Review (SLR) alongside a comprehensive regulatory document review, this research develops a governance model that balances business objectives with legal compliance. The proposed Agile Data Commercialization Governance Framework (ADCGF) simplifies governance by emphasizing three essential DMBOK v2 pillars: structured data governance (Data Governance), data quality validation against set benchmarks (Data Quality), and de-identification techniques such as anonymization and aggregation (Data Security & Privacy). This framework suggests that privacy regulations can act as facilitators for startup innovation and market trust, offering practical guidance for digital businesses to exploit data's economic value securely and compliantly.]]>
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